Adv Sci (Weinh). 2025 May 8:e2415698. doi: 10.1002/advs.202415698. Online ahead of print.

ABSTRACT

Peripheral T-cell lymphoma (PTCL) is a heterogeneous group of mature T-cell malignancies with poor prognosis. Therefore, improved therapies are urgently required to improve patient outcomes. In this study, metabolic inhibitor drug screening reveals that quinacrine elicits excellent antitumor activity both in vitro and in vivo by downregulating intracellular arginine levels in PTCL. Single-cell transcriptomic analyses reveal aberrant arginine metabolism in patients with PTCL, characterized by excessive solute carrier family 3 member 2 (SLC3A2) mediated arginine uptake preferentially in tumor cells. High SLC3A2 expression predicts poor outcomes in PTCL, as SLC3A2-mediated arginine uptake promotes the malignant behaviors of tumor cells and induces tumor immune escape, thereby fueling tumor progression. Mechanistically, high arginine levels induce global metabolic changes, including enhanced oxidative phosphorylation by promoting nascent RNA synthesis. This work identifies structure-specific recognition protein 1 (SSRP1), which upregulates SLC3A2, as a co-transcription factor with JUNB. Quinacrine disrupts SLC3A2-mediated arginine transport by targeting SSRP1. Combining quinacrine with histone deacetylase inhibitors is a promising therapeutic strategy for PTCL.

PMID:40344476 | DOI:10.1002/advs.202415698

J Cancer Res Clin Oncol. 2025 May 9;151(5):157. doi: 10.1007/s00432-025-06185-y.

ABSTRACT

PURPOSE: Drug repurposing may be an efficient strategy for identifying new cancer treatments. Tranexamic acid (TXA), an antifibrinolytic agent that affects the plasminogen-plasmin pathway, may have potential anticancer effects by influencing tumor cell proliferation, angiogenesis, inflammation, immune response, and tissue remodeling-all crucial processes contributing to tumor progression and metastasis.

OBJECTIVE: Evaluate TXA's anticancer effects across in vitro, animal, and clinical studies to assess its potential as a repurposed cancer drug.

METHODS: The study was designed as a PRISMA-compliant systematic review and meta-analysis. The literature search was conducted in MEDLINE, EMBASE, Web of Science, and the Cochrane Library. In vitro, animal, and clinical studies investigating the anticancer effects of TXA or epsilon-aminocaproic acid (EACA) were included. Animal and clinical studies were critically appraised, and studies with a low risk of bias were included in the meta-analysis.

RESULTS: Of 4367 identified records, 38 articles were included, collectively reporting findings from 41 in vitro studies, 34 animal studies (n = 843 animals), and seven clinical studies (n = 91 patients). The meta-analysis included nine animal studies and showed a tumor growth reduction in animals treated with TXA compared to controls with a standardized mean difference of - 1.0 (95%CI - 1.5; - 0.4) (p = 0.0002). Equivalently, the majority of in vitro studies reported reduced proliferation, viability, and invasiveness in TXA-exposed tumor cell lines. The clinical studies were considerably susceptible to bias, rendering any conclusions futile.

CONCLUSIONS: TXA shows promise as a repurposed cancer drug, revealing an overall reduction in tumor growth, viability, and invasiveness in animal and in vitro studies.

PMID:40343490 | DOI:10.1007/s00432-025-06185-y

CNS Neurosci Ther. 2025 May;31(5):e70411. doi: 10.1111/cns.70411.

ABSTRACT

BACKGROUND: Research on animal models of neurological diseases has primarily focused on understanding pathogenic mechanisms, advacing diagnostic strateggies, developing pharmacotherapies, and exploring preventive interventions. To facilitate comprehensive and systematic studies in this filed, we have developed the Neurological Disease Animal Model Database (ND-AMD), accessible at https://www.uc-med.net/NDAMD. This database is signed around the central theme of "Big Data - Neurological Diseases - Animal Models - Mechanism Research," integrating large-scale, multi-dimensional, and multi-scale data to facilitate in-depth analyses. ND-AMD serves as a resource for panoramic studies, enabling comparative and mechanistic research across diverse experimental conditions, species, and disease models.

METHOD: Data were systematically retrieved from PubMed, Web of Science, and other relevant databases using Boolean search strategies with standardized MeSH terms and keywords. The collected data were curated and integrated into a structured SQL-based framework, ensuring consistency through automated validation checks and manual verification. Heterogeneity and sensitivity analyses were conducted using Cochran's Q test and the I2 statistic to assess variability across studies. Statistical workflows were implemented in Python (SciPy, Pandas, NumPy) to support multi-scale data integration, trend analysis, and model validation. Additionally, a text co-occurrence network analysis was performed using Natural Language Processing (TF-IDF and word embeddings) to identify key conceptual linkages and semantic structures across studies.

RESULTS: ND-AMD integrates data from 483 animal models of neurological diseases, covering eight disease categories, 21 specific diseases, 13 species, and 152 strains. The database provides a comprehensive repository of experimental and phenotypic data, covering behavioral, physiological, biochemical, molecular pathology, immunological, and imaging characteristics. Additionally, it incorporates application-oriented data, such as drug evaluation outcomes. To enhance data accessibility and facilitate in-depth analysis, ND-AMD features three custom-developed online tools: Model Frequency Analysis, Comparative Phenotypic Analysis, and Bibliometric Analysis, enabling systematic comparison and trend identification across models and experimental conditions.

CONCLUSIONS: The centralized feature of ND-AMD enables comparative analysis across different animal models, strains, and experimental conditions. It helps capture intricate interactions between biological systems at different levels, ranging from molecular mechanisms to cellular processes, neural networks, and behavioral outcomes. These models play a vital role as tools in replicating pathological conditions of neurological diseases. By offering users convenient, efficient, and intuitive access to data, ND-AMD enables researchers to identify patterns, trends, and potential therapeutic targets that may not be apparent in individual studies.

PMID:40344361 | DOI:10.1111/cns.70411

Genetics. 2025 May 9:iyaf088. doi: 10.1093/genetics/iyaf088. Online ahead of print.

ABSTRACT

Biobanks linking genetic data with clinical health records provide exciting opportunities for pharmacogenomic (PGx) research on genetic variation and drug response. Designed as central and multi-use resources, biobanks can facilitate diverse PGx research efforts, including the study of drug efficacy and adverse effects. Specialized PGx alleles and phenotypes are critical for such studies and can be conveniently called from existing array-based genotypes routinely collected in most biobanks. We describe a central callset of PGx alleles and phenotypes in over 80,000 participants of the Michigan Genomics Initiative (MGI) biobank, created using the PyPGx software on TOPMed imputed genotypes. The array-based PGx allele calls demonstrate concordance (>92%) with a set of PCR-validated alleles collected during clinical care, but do not identify PGx alleles dependent on structural variation, including the clinically important CYP2D6*5 deletion. To address this, we developed a support vector machine trained on genotype array SNV probe intensities to classify CYP2D6*5 carriers. This method had >99% accuracy and reclassified ∼7% of African American and ∼4% of White MGI participants to lower activity metabolizer phenotypes, predicting higher risks of adverse drug reactions. We demonstrate that central PGx callsets created with existing tools and genetic data can be augmented by customized calls for challenging alleles based on structural variants to broaden the research potential and clinical utility of biobanks. These PGx callsets can be created in biobanks with existing array-based genotype data and highlight the utility of advanced computational methods in PGx allele identification.

PMID:40344017 | DOI:10.1093/genetics/iyaf088

Head Neck. 2025 May 8. doi: 10.1002/hed.28179. Online ahead of print.

ABSTRACT

BACKGROUND: This study investigated the roles of single nucleotide variants (SNVs) in genes of CDDP metabolism and their association with kidney dysfunction in patients with head and neck squamous cell carcinoma (HNSCC).

METHODS: A total of 109 patients with locally advanced HNSCC, treated with CDDP, had renal function evaluated by serum creatinine level and CKD-EPI formula, and underwent genotyping by polymerase chain reaction.

RESULTS: Patients with GSTT1 present and ERCC1 c.354CT or TT genotypes showed 4.94% and 8.94% renal function reduction, respectively. GSTT1 present with TP53 c.215G>C (17.67%), GSTP1 c.313A>G with ERCC1 c.354C>T (17.57%), GSTP1 c.313A>G with MLH1 c.93G>A (12.49%), GSTP1 c.313A>G with MSH3 c.3133A>G (12.19%), ERCC1 c.354C>T with MLH1 c.93G>A (18.85%) and ERCC1 c.354C>T with MSH3 c.3133A>G (13.38%) combined genotypes were also associated with substantial declines in renal function.

CONCLUSIONS: Our data suggest that isolated and combined SNVs in genes enrolled in CDDP metabolism can be used to select patients for treatments that spare the kidneys from adverse effects.

PMID:40342074 | DOI:10.1002/hed.28179

ACS Chem Biol. 2025 May 9. doi: 10.1021/acschembio.4c00562. Online ahead of print.

ABSTRACT

The fungus Aspergillus fumigatus and the bacterium Burkholderia cenocepacia cause fatal respiratory infections in immunocompromised humans and patients with lung disease, such as cystic fibrosis (CF). In dual infections, antagonistic interactions contribute to increased mortality. These interactions are further altered by the presence of antimicrobial and antifungal agents. However, studies performed to date on chemical interactions between clinical B. cenocepacia and A. fumigatus have focused on pathogens in isolation and do not include the most abundant chemical signal, i.e., clinically administered therapeutics, present in the lung. Here, we characterize small molecule-mediated interactions between B. cenocepacia and A. fumigatus and their shift in response to trimethoprim exposure by using metabolomics and mass spectrometry imaging. Using these methods, we report that the production of several small-molecule natural products of both the bacteria and the fungus is affected by cocultivation and exposure to trimethoprim. By systematic analysis of metabolomics data, we hypothesize that the B. cenocepacia-encoded ham gene cluster plays a role in the trimethoprim-mediated alteration of bacterial-fungal interactions. We support our findings by generating a genetically modified strain lacking the ham gene cluster and querying its interaction with A. fumigatus. Using comparative analyses of the extracts of wild-type and knockout strains, we report the inactivation of a bacterially produced antifungal compound, fragin, by A. fumigatus, which was verified by the addition of purified fragin to the A. fumigatus culture. Furthermore, we report that trimethoprim does not inhibit fungal growth, but affects the biochemical pathway for DHN-melanin biosynthesis, an important antifungal drug target, altering the pigmentation of the fungal conidia and is associated with modification of ergosterol to ergosteryl-3β-O-l-valine in coculture. This study demonstrates the impact of therapeutics on shaping microbial and fungal metabolomes, which influence interkingdom interactions and the expression of virulence factors. Our findings enhance the understanding of the complexity of chemical interactions between therapeutic compounds, bacteria, and fungi and may contribute to the development of selective treatments.

PMID:40344688 | DOI:10.1021/acschembio.4c00562

Ann Am Thorac Soc. 2025 May 9. doi: 10.1513/AnnalsATS.202501-093OC. Online ahead of print.

ABSTRACT

RATIONALE: FEV1 and its longitudinal change are mortality risk-factors. Visit-to-visit-FEV1-variation is a risk-factor for death in cystic fibrosis but has not been studied in other cohorts.

OBJECTIVE: Assess if longitudinal visit-to-visit-FEV1-variation is a mortality risk-factor in World Trade Center (WTC) exposed FDNY rescue/recovery workers.

METHODS: Linear mixed-effects regression of all post-9/11/2001 FEV1 measurements defined time-effect on longitudinal-FEV1-decline (FEV1-slope) and its standard error (visit-to-visit-FEV1-variation). Cox proportional hazards and logistic models adjusted for age and smoking assessed the association between FEV1 related risk-factors and mortality. Receiver operating characteristic area under the curve (ROC-AUC) assessed predictive model performance.

MEASUREMENTS AND MAIN RESULTS: Among 11,745 workers with ≥3 FEV1 measurements, 575 (4.9%) died. When all FEV1-related risk-factors were combined, each 5 mL/year increase in visit-to-visit-FEV1-variation increased mortality 2.1-fold (HR=2.14; 95%CI=1.84-2.48); each 10%predicted reduction in the last-longitudinal-FEV1 increased mortality 15% (HR=1.15; 95%CI=1.09-1.21), but each 10ml/year longitudinal-FEV1-decline was not associated with mortality (HR=1.04; 95%CI=0.99-1.10). The ROC-AUC of a fully adjusted multivariable cumulative mortality model was 0.82 (95%CI=0.80-0.84); for unadjusted visit-to-visit-FEV1-variation, AUC was 0.80 (95%CI=0.78-0.82); for last-longitudinal-FEV1 AUC was 0.61 (95%CI=0.59-0.64) and for longitudinal-FEV1-decline AUC was 0.58 (95%CI=0.56-0.61). In the 1,988/11,745(16.9%) with high-WTC-exposure defined as arrival at the WTC-site before noon on 9/11/2001, the risk of high-visit-to-visit-FEV1-variation (top-quartile, ≥10.35 ml/year) increased 25% (OR=1.25; 95%CI=1.12-1.40).

CONCLUSIONS: Visit-to-visit-FEV1-variation is a mortality risk-factor in FDNY rescue and recovery workers with greater accuracy for predicting cumulative mortality than either last-longitudinal-FEV1 or longitudinal-FEV1-decline. Further investigation in other cohorts is needed to assess the generalizability of this rarely studied mortality risk-factor.

PMID:40343979 | DOI:10.1513/AnnalsATS.202501-093OC

Biol Reprod. 2025 May 9:ioaf103. doi: 10.1093/biolre/ioaf103. Online ahead of print.

ABSTRACT

Cervical mucus changes throughout the menstrual cycle in response to hormonal fluctuations, regulating access of sperm and pathogens to the reproductive tract. CFTR is an anion channel that plays a critical role in mediating epithelial mucus secretions. Primary endocervical cells obtained from rhesus macaques Macaca mulatta were cultured using conditional reprogramming and treated with vehicle controls or CFTR inhibitors. In order to measure changes in hydration and viscosity of secreted mucus, we adapted two airway mucus assays, airway surface liquid and particle-tracking microrheology, for our endocervical culture system. Endocervical cells treated with CFTR inhibitors demonstrated dehydrated, thicker mucus secretions compared to controls in both assay outputs. Our studies suggest that CFTR may be an important mediator of fertility changes and provide experimental evidence for the infertility phenotype seen in women with cystic fibrosis. Additionally, assays developed in these studies provide new endpoints for assessing cervical mucus changes in vitro.

PMID:40342009 | DOI:10.1093/biolre/ioaf103

Eur J Radiol. 2025 Apr 23;188:112132. doi: 10.1016/j.ejrad.2025.112132. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aims to investigate the impact of adaptive statistical iterative reconstruction-Veo (ASIR-V) and deep learning image reconstruction (DLIR) algorithms on the quantification of pericoronary adipose tissue (PCAT) and epicardial adipose tissue (EAT). Furthermore, we propose to explore the feasibility of correcting the effects through fat threshold adjustment.

METHODS: A retrospective analysis was conducted on the imaging data of 134 patients who underwent coronary CT angiography (CCTA) between December 2023 and January 2024. These data were reconstructed into seven datasets using filtered back projection (FBP), ASIR-V at three different intensities (ASIR-V 30%, ASIR-V 50%, ASIR-V 70%), and DLIR at three different intensities (DLIR-L, DLIR-M, DLIR-H). Repeated-measures ANOVA was used to compare differences in fat, PCAT and EAT attenuation values among the reconstruction algorithms, and Bland-Altman plots were used to analyze the agreement between ASIR-V or DLIR and FBP algorithms in PCAT attenuation values.

RESULTS: Compared to FBP, ASIR-V 30 %, ASIR-V 50 %, ASIR-V 70 %, DLIR-L, DLIR-M, and DLIR-H significantly increased fat attenuation values (-103.91 ± 12.99 HU, -102.53 ± 12.68 HU, -101.14 ± 12.78 HU, -101.81 ± 12.41 HU, -100.87 ± 12.25 HU, -99.08 ± 12.00 HU vs. -105.95 ± 13.01 HU, all p < 0.001). When the fat threshold was set at -190 to -30 HU, ASIR-V and DLIR algorithms significantly increased PCAT and EAT attenuation values compared to FBP algorithm (all p < 0.05), with these values increasing as the reconstruction intensity level increased. After correction with a fat threshold of -200 to -35 HU for ASIR-V 30 %, -200 to -40 HU for ASIR-V 50 % and DLIR-L, and -200 to -45 HU for ASIR-V 70 %, DLIR-M, and DLIR-H, the mean differences in PCAT attenuation values between ASIR-V or DLIR and FBP algorithms decreased (-0.03 to 1.68 HU vs. 2.35 to 8.69 HU), and no significant difference was found in PCAT attenuation values between FBP and ASIR-V 30 %, ASIR-V 50 %, ASIR-V 70 %, DLIR-L, and DLIR-M (all p > 0.05).

CONCLUSION: Compared to the FBP algorithm, ASIR-V and DLIR algorithms increase PCAT and EAT attenuation values. Adjusting the fat threshold can mitigate the impact of ASIR-V and DLIR algorithms on PCAT attenuation values.

PMID:40344712 | DOI:10.1016/j.ejrad.2025.112132

PLoS Comput Biol. 2025 May 9;21(5):e1013074. doi: 10.1371/journal.pcbi.1013074. Online ahead of print.

ABSTRACT

With a burgeoning number of artificial intelligence (AI) applications in various fields, biomolecular science has also given a big welcome to advanced AI techniques in recent years. In this broad field, scoring a protein-ligand binding structure to output the binding strength is a crucial problem that heavily relates to computational drug discovery. Aiming at this problem, we have proposed an efficient scoring framework using deep learning techniques. This framework describes a binding structure by a high-resolution atomic graph, places a focus on the inter-molecular interactions and learns the graph in a rational way. For a protein-ligand binding complex, the generated atomic graph reserves key information of the atoms (as graph nodes), and focuses on inter-molecular interactions (as graph edges) that can be identified by introducing multiple distance ranges to the atom pairs within the binding area. To provide more confidence in the predicted binding strengths, we have interpreted the deep learning model from the model level and in a post-hoc analysis. The proposed learning framework has been demonstrated to have competitive performances in scoring and screening tasks, which will prospectively promote the development of related fields further.

PMID:40344574 | DOI:10.1371/journal.pcbi.1013074

JCO Clin Cancer Inform. 2025 May;9:e2400284. doi: 10.1200/CCI-24-00284. Epub 2025 May 9.

ABSTRACT

PURPOSE: Artificial intelligence (AI) tools could improve clinical decision making or exacerbate inequities because of bias. African American (AA) men reportedly have a worse prognosis for prostate cancer (PCa) and are underrepresented in the development genomic biomarkers. We assess the generalizability of tools developed using a multimodal AI (MMAI) deep learning system using digital histopathology and clinical data from NRG/Radiation Therapy Oncology Group PCa trials across racial subgroups.

METHODS: In total, 5,708 patients from five randomized phase III trials were included. Two MMAI algorithms were evaluated: (1) the distant metastasis (DM) MMAI model optimized to predict risk of DM, and (2) the PCa-specific mortality (PCSM) MMAI model optimized to focus on prediction death in the presence of DM (DDM). The prognostic performance of the MMAI algorithms was evaluated in AA and non-AA subgroups using time to DM (primary end point) and time to DDM (secondary end point). Exploratory end points included time to biochemical failure and overall survival with Fine-Gray or Cox proportional hazards models. Cumulative incidence estimates were computed for time-to-event end points and compared using Gray's test.

RESULTS: There were 948 (16.6%) AA patients, 4,731 non-AA patients (82.9%), and 29 (0.5%) patients with unknown or missing race status. The DM-MMAI algorithm showed a strong prognostic signal for DM in the AA (subdistribution hazard ratio [sHR], 1.2 [95% CI, 1.0 to 1.3]; P = .007) and non-AA subgroups (sHR, 1.4 [95% CI, 1.3 to 1.5]; P < .001). Similarly, the PCSM-MMAI score showed a strong prognostic signal for DDM in both AA (sHR, 1.3 [95% CI, 1.1 to 1.5]; P = .001) and non-AA subgroups (sHR, 1.5 [95% CI, 1.4 to 1.6]; P < .001), with similar distributions of risk.

CONCLUSION: Using cooperative group data sets with a racially diverse population, the MMAI algorithm performed well across racial subgroups without evidence of algorithmic bias.

PMID:40344545 | DOI:10.1200/CCI-24-00284

Adv Sci (Weinh). 2025 May 8:e2501269. doi: 10.1002/advs.202501269. Online ahead of print.

ABSTRACT

The CRISPR-Cas12a system has gained significant attention as a rapid nucleic acid diagnostic tool due to its crRNA-guided trans-cleavage activity. Accurately predicting the activity of different targets is significant to facilitate the crRNA availability but remains challenging. In this study, a novel approach is presented that combines molecular dynamics simulations and neural network modeling to predict the trans-cleavage activity. Unlike conventional tools that rely solely on the base sequences, our method integrated sequence features and molecular interaction features of DNA in the CRISPR-Cas12a system, significantly improving prediction accuracy. Through feature importance analysis, key sequence features that influence Cas12a trans-cleavage activity are identified. Additionally, a crRNA-DNA library with over 23 456 feature sequences from representative viruses and bacteria is established, and validated the high predictive accuracy of the model (Pearson's r = 0.9328) by screening crRNAs from reference targets. This study offers new insights into the molecular interactions of Cas12a/crRNA-DNA and provides a reliable framework for optimizing crRNA design, facilitating the application of the CRISPR-Cas12a in rapid nucleic acid diagnostics.

PMID:40344384 | DOI:10.1002/advs.202501269

J Proteome Res. 2025 May 9. doi: 10.1021/acs.jproteome.4c00973. Online ahead of print.

ABSTRACT

Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related to peptide sequences and their resulting physicochemical properties. Moreover, the high variability in MS data challenges the development of a generic model for detectability prediction, underlining the need for customizable tools. We present Pfly, a deep learning model developed to predict peptide detectability based solely on peptide sequence. Pfly is a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor Pfly to specific experimental conditions, improving accuracy and expanding applicability across various research fields. Pfly is an encoder-decoder with an attention mechanism, classifying peptides as flyers or non-flyers, and providing both binary and categorical probabilities for four distinct classes defined in this study. The model was initially trained on a synthetic peptide library and subsequently fine-tuned with a biological dataset to mitigate bias toward synthesizability, improving predictive capacity and outperforming state-of-the-art predictors in benchmark comparisons across different human and cross-species datasets. The study further investigates the influence of protein abundance and rescoring, illustrating the negative impact on peptide identification due to misclassification. Pfly has been integrated into the DLOmix framework and is accessible on GitHub at https://github.com/wilhelm-lab/dlomix.

PMID:40344201 | DOI:10.1021/acs.jproteome.4c00973

Med Phys. 2025 May 8. doi: 10.1002/mp.17871. Online ahead of print.

ABSTRACT

BACKGROUND: Cardiac perfusion PET is commonly used to assess ischemia and cardiovascular risk, which enables quantitative measurements of myocardial blood flow (MBF) through kinetic modeling. However, the estimation of kinetic parameters is challenging due to the noisy nature of short dynamic frames and limited sample data points.

PURPOSE: This work aimed to investigate the errors in MBF estimation in PET through a simulation study and to evaluate different parameter estimation approaches, including a deep learning (DL) method.

MATERIALS AND METHODS: Simulated studies were generated using digital phantoms based on cardiac segmentations from 55 clinical CT images. We employed the irreversible 2-tissue compartmental model and simulated dynamic 13N-ammonia PET scans under both rest and stress conditions (220 cases each). The simulations covered a rest K1 range of 0.6 to 1.2 and a stress K1 range of 1.2 to 3.6 (unit: mL/min/g) in the myocardium. A transformer-based DL model was trained on the simulated dataset to predict parametric images (PIMs) from noisy PET image frames and was validated using 5-fold cross-validation. We compared the DL method with the voxel-wise nonlinear least squares (NLS) fitting applied to the dynamic images, using either Gaussian filter (GF) smoothing (GF-NLS) or a dynamic nonlocal means (DNLM) algorithm for denoising (DNLM-NLS). Two patients with coronary CT angiography (CTA) and fractional flow reserve (FFR) were enrolled to test the feasibility of applying DL models on clinical PET data.

RESULTS: The DL method showed clearer image structures with reduced noise compared to the traditional NLS-based methods. In terms of mean absolute relative error (MARE), as the rest K1 values increased from 0.6 to 1.2 mL/min/g, the overall bias in myocardium K1 estimates decreased from approximately 58% to 45% for the NLS-based methods while the DL method showed a reduction in MARE from 42% to 18%. For stress data, as the stress K1 decreased from 3.6 to 1.2 mL/min/g, the MARE increased from 30% to 70% for the GF-NLS method. In contrast, both the DNLM-NLS (average: 42%) and the DL methods (average: 20%) demonstrated significantly smaller MARE changes as stress K1 varied. Regarding the regional mean bias (±standard deviation), the GF-NLS method had a bias of 6.30% (±8.35%) of rest K1, compared to 1.10% (±8.21%) for DNLM-NLS and 6.28% (±14.05%) for the DL method. For the stress K1, the GF-NLS showed a mean bias of 10.72% (±9.34%) compared to 1.69% (±8.82%) for DNLM-NLS and -10.55% (±9.81%) for the DL method.

SIGNIFICANCE: This study showed that an increase in the tracer uptake rate (K1) corresponded to improved accuracy and precision in MBF quantification, whereas lower tracer uptake resulted in higher noise in dynamic PET and poorer parameter estimates. Utilizing denoising techniques or DL approaches can mitigate noise-induced bias in PET parametric imaging.

PMID:40344168 | DOI:10.1002/mp.17871

PLoS One. 2025 May 9;20(5):e0322624. doi: 10.1371/journal.pone.0322624. eCollection 2025.

ABSTRACT

Brain tumors pose a significant medical challenge, necessitating early detection and precise classification for effective treatment. This study aims to address this challenge by introducing an automated brain tumor classification system that utilizes deep learning (DL) and Magnetic Resonance Imaging (MRI) images. The main purpose of this research is to develop a model that can accurately detect and classify different types of brain tumors, including glioma, meningioma, pituitary tumors, and normal brain scans. A convolutional neural network (CNN) architecture with pretrained VGG16 as the base model is employed, and diverse public datasets are utilized to ensure comprehensive representation. Data augmentation techniques are employed to enhance the training dataset, resulting in a total of 17,136 brain MRI images across the four classes. The accuracy of this model was 99.24%, a higher accuracy than other similar works, demonstrating its potential clinical utility. This higher accuracy was achieved mainly due to the utilization of a large and diverse dataset, the improvement of network configuration, the application of a fine-tuning strategy to adjust pretrained weights, and the implementation of data augmentation techniques in enhancing classification performance for brain tumor detection. In addition, a web application was developed by leveraging HTML and Dash components to enhance usability, allowing for easy image upload and tumor prediction. By harnessing artificial intelligence (AI), the developed system addresses the need to reduce human error and enhance diagnostic accuracy. The proposed approach provides an efficient and reliable solution for brain tumor classification, facilitating early diagnosis and enabling timely medical interventions. This work signifies a potential advancement in brain tumor classification, promising improved patient care and outcomes.

PMID:40344143 | DOI:10.1371/journal.pone.0322624

PLoS One. 2025 May 9;20(5):e0322365. doi: 10.1371/journal.pone.0322365. eCollection 2025.

ABSTRACT

PURPOSE: Radiomics allows for the quantification of medical images and facilitates precision medicine. Many radiomic features derived from computed tomography (CT) are sensitive to variations across scanners, reconstruction settings, and acquisition protocols. In this phantom study, eight different CT reconstruction parameters were varied to explore image- and feature-level harmonization approaches to improve tissue classification.

METHODS: Varying reconstructions of an anthropomorphic radiopaque phantom containing three lesion categories (metastasis, hemangioma, and benign cyst) and normal liver tissue were used for evaluating two harmonization methods and their combination: (i) generative adversarial networks (GANs) at the image level; (ii) ComBat at the feature level, and (iii) a combination of (i) and (ii). A total of 93 texture and intensity features were extracted from each tissue class before and after image-level harmonization and were also harmonized at the feature level. Reproducibility and stability were assessed via the Concordance Correlation Coefficient (CCC) and pairwise comparisons using paired stability tests. The ability of features to discriminate between tissue classes was assessed by measuring the area under the receiver operating characteristic curve. The global reproducibility and discriminative power were assessed by averaging over the entire dataset and across all tissue types.

RESULTS: ComBat improved reproducibility by 31.58% and stability by 5.24%, while GAN increased reproducibility by 8% it reduced stability by 4.33%. Classification analysis revealed that ComBat increased average AUC by 15.19%, whereas GAN decreased AUC by 2.56%.

CONCLUSION: While GAN qualitatively enhances image harmonization, ComBat provides superior statistical improvements in feature stability and classification performance, highlighting the importance of robust feature-level harmonization in radiomics.

PMID:40344028 | DOI:10.1371/journal.pone.0322365

J Xray Sci Technol. 2025 May;33(3):501-515. doi: 10.1177/08953996241304987. Epub 2025 Feb 20.

ABSTRACT

BackgroundLung disease is the crucial disease that affects the breathing conditions and even causes death. There are various approaches for the lung disease classification; still the inefficiency in accurate detection, computational complexity and over-fitting issues limits the performance of the model. To overcome the challenges, a deep learning model is proposed in this research. Initially, the input is acquired and is pre-processed using three various techniques like data augmentation, filtering and image re-sizing. Then, the threshold based segmentation is employed for obtaining the required region.ObjectiveFrom the segmented image, various categories of lung diseases like COVID, lung Opacity, Pneumonia and normal are identified using the proposed Optimal Cross Stage Partial Bidirectional Long short term memory (OCBiNet).MethodsThe proposed OCBiNet is designed using Bidirectional Long short-term memory (BiNet) with Cross Stage Partial connection in its hidden state. Besides, the adjustable parameters are modified using the proposed Improved Mother Optimization (ImMO) algorithm.ResultsThe ImMO algorithm is designed by integrating the Logistic Chaotic Mapping within the conventional Mother Optimization algorithm for enhancing the convergence rate in obtaining the global best solution.ConclusionsThe proposed OCBiNet is evaluated based on Accuracy, Recall, Precision, and F-Score and acquired the values of 99.11%, 98.98%, 99.18%, and 99.08% respectively.

PMID:40343884 | DOI:10.1177/08953996241304987

IEEE Trans Biomed Eng. 2025 May 9;PP. doi: 10.1109/TBME.2025.3568282. Online ahead of print.

ABSTRACT

Deep learning has significantly enhanced the research on the emerging issue of Electroencephalogram (EEG)-based visual classification and reconstruction, which has gained a growth of attention and concern recently. To promote the research progress, at this critical moment, a review work on the deep learning methodology for the issue becomes necessary and important. However, such a work seems absent in the literature. This paper provides the first review on EEG-based visual classification and reconstruction, whose contents can be categorized into the following four main parts: 1) comprehensively summarizing and systematically analyzing the representative deep learning methods from both feature encoding and decoding perspectives; 2) introducing the available benchmark datasets, describing the experimental paradigms, and displaying the method performances; 3) proposing the methodological essences and neuroscientific insights as well as the dynamic closed-loop interaction and promotion between them, which are potentially beneficial for technological innovations and academic progress; 4) discussing the potential challenges of current research and the prospective opportunities in future trends. We expect that this work can shed light on the technological directions and also enlighten the academic breakthroughs for the issue in the not-so-far future.

PMID:40343828 | DOI:10.1109/TBME.2025.3568282

Prim Care Companion CNS Disord. 2025 May 1;27(2):24cr03883. doi: 10.4088/PCC.24cr03883.

NO ABSTRACT

PMID:40344540 | DOI:10.4088/PCC.24cr03883

J Appl Toxicol. 2025 Jun;45(6):948-963. doi: 10.1002/jat.4758. Epub 2025 Jan 30.

ABSTRACT

4,4'-Diaminodiphenylmethane (DAPM) is an aromatic amine used in the industrial synthesis of polyurethane. In rats, acute DAPM exposure induces biliary epithelial cell injury in the liver, but subchronic exposure promotes a female-specific pulmonary arterial hypertension (PAH). PAH in humans is four times more prevalent in women than men. To shed light on mechanisms explaining the female selectivity of PAH in humans, we examined molecular pathways underlying DAPM-induced PAH in female rats. Intersections between DAPM-mediated hepatic injury and DAPM-induced PAH were also interrogated. Intact compared to ovariectomized female rats were gavaged once weekly for 12 weeks with DAPM or vehicle. Morphometric analysis in lung sections was used to quantify PAH pathology. mRNA from liver were assessed for DAPM-induced alterations in genes associated with aryl hydrocarbon receptor, estrogen response, and endothelin-1 signaling. mRNA from pulmonary arteries were subjected to transcript profiling, and pathways associated with differentially expressed genes were mapped. First, DAPM-induced PAH was exacerbated by ovariectomy. Although DAPM-mediated liver injury per se was not correlated with its induction of PAH, increases in levels of the potent vasoconstrictor endothelin-1 were exacerbated by ovariectomy and were correlated with increased expression of Edn1 in the liver. In pulmonary arteries, transcript profiling revealed that DAPM and ovariectomy interacted to dysregulate estrogen receptor, VEGF, PI3K/AKT, endothelin-1, glucocorticoid receptor, IL-17A, and idiopathic pulmonary fibrosis signaling. One of the most dysregulated genes associated with both DAPM and estrogen status was Serpine1.

PMID:40344275 | DOI:10.1002/jat.4758